Recurrent neural network for adaptive cyber attack prediction on critical defense systems
Keywords:
Adaptive Intrusion Detection, Critical Defense System, Cyber Attack Prediction, RNNAbstract
The threat of cyber attacks against critical defense systems is becoming increasingly complex and dynamic, requiring adaptive and proactive prediction mechanisms. This study aims to develop a Recurrent Neural Network (RNN) model to predict cyber attacks on critical defense systems with high accuracy and generalization capabilities against new attacks. The CICIDS2020 dataset was used to train and test the model, with 70% of the data allocated for training, 15% for validation, and 15% for testing. The RNN architecture was optimized by selecting the number of hidden layers, the number of neurons per layer, the activation function, and the application of dropout and regularization to minimize the risk of overfitting. The model was trained using the Backpropagation Through Time (BPTT) algorithm and evaluated using accuracy, precision, recall, F1-score, and AUC metrics. The results show that RNN outperforms LSTM, Random Forest, and SVM algorithms, with an accuracy of 97.8%, precision of 96.5%, recall of 95.9%, F1-score of 96.2%, and AUC of 0.981, and is capable of detecting rare attacks. These findings confirm the effectiveness of RNN in capturing long-term temporal patterns in cyberattack data and providing adaptive predictions for new attacks. The practical implications of this research include strengthening critical defense systems through early detection and real-time mitigation of cyberattacks, as well as providing a basis for the development of reliable proactive security systems.
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